Lexical Based Semantic Orientation of Online Customer Reviews and Blogs

TL;DR

This paper presents a domain-independent, lexical-based sentiment classification method for online reviews and blogs, leveraging general lexicons to improve portability and outperform machine learning approaches in accuracy.

Contribution

The paper introduces a novel sentence-level sentiment analysis technique using WordNet, SentiWordNet, and user-defined lexicons, addressing domain portability issues.

Findings

Achieved 87% precision at document level

Achieved 83% precision at sentence level

Outperforms traditional corpus-based machine learning methods

Abstract

Rapid increase in internet users along with growing power of online review sites and social media has given birth to sentiment analysis or opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. This paper proposed sentence-level lexical based domain independent sentiment classification method for different types of data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for semantic orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The method performs better than word and text level corpus based machine learning methods for semantic orientation. The results show the proposed method performs better as it shows precision of 87% and83% at document and sentence levels respectively for online comments.